Optical spectroscopy can be used to determine the concentration of chemical species in gaseous, liquid, and solid samples. The amount of light absorbed by a particular chemical species is often linearly related to its concentration through Beer's Law, A=εlc, where A is the absorbance of the chemical species, ε is a constant specific to the chemical, l is the path length of light, and c is the concentration of the chemical. When incident light with an intensity, I0, is incident on the sample, and I is the intensity of light after it has passed through a solution containing the chemical to be measured, the absorbance is given by A=log (I0/I).
For nontransparent materials, including complex materials such as powders, tablets, natural materials (e.g., soil, agricultural products), blood, skin, and muscle, optical information can be collected via diffuse reflectance spectroscopy. In this setting, A=(I100/IR), where I100 is the light reflected from a 100% diffuse reflectance standard, the equivalent of the incident light, and IR is the light reflected from the sample under study. The concentration of a chemical component in one of these complex materials is related to A, though often not linearly. More sophisticated mathematical techniques, such as, for example, partial least squares regression and other multivariate calibration methods are used to determine a relationship between concentration and absorbance. Once these calibration models are derived, they can be used to determine chemical composition by measuring absorbance in transmittance or reflectance mode.
Diffuse reflectance spectroscopy techniques with near infrared spectroscopy (“NIRS”) have been used for the noninvasive measurement of blood and tissue chemistry in human and animal subjects. NIRS (e.g., using a wavelength range of about 650-1000 nm) can be used to measure a number of important medical parameters such as tissue oxygenation, tissue pH, blood hematocrit (“Hct”), and glucose, but its widespread application in medicine has been hindered by both the inter- and intra-subject analyte-irrelevant variation in tissue overlying as well as inside the structures to be measured. For example, when diffuse reflectance NIRS is used to measure blood hematocrit in muscle or organs, the accuracy of the measurements can be affected by absorbance variations in layers overlying the muscle or organs (e.g., due to variations in the thickness of fat and skin layers between different patients in a patient population or between different locations on an individual patient) and/or spectral interference from structural variations in muscle and/or organs that are irrelevant to the measurements.
Near infrared light can penetrate through a subject's skin and bone to provide information on chemical species present in blood and underlying tissue. For example, pulse oximetry, a ubiquitous hospital monitoring system that measures arterial hemoglobin oxygen saturation, is based on two-wavelength NIRS. Multi-wavelength NIRS, in combination with chemometrics (i.e., statistics-based methods of analyzing complex spectra), can provide a platform technology for the noninvasive measurement of several additional analytes present in the blood and tissue. NIRS can provide accurate and continuous measurement of medical analytes without the need to remove a blood or tissue sample from the patient. The application of this technique involves shining near infrared light onto the skin directly or through a fiber optic bundle and measuring the spectrum of the light that is reflected back from the blood containing muscle. While near-infrared absorption by hemoglobin and deoxyhemoglobin has been used to measure oxygen saturation levels in various tissue beds, multi-wavelength spectroscopy in combination with additional mathematical techniques often is required to measure additional important blood and tissue analytes. Chemometrics is a branch of chemistry that provides statistics-based techniques to process multi-wavelength spectra such that analyte concentration can be calculated from the reflectance spectra recorded from complex media such as biological tissue.
Chemometrics is used to derive a mathematical relationship between relevant portions of the spectra collected from a sample and the concentration or amount of the analyte of interest in the sample. The relationship between the spectra and the chemical concentration can be expressed as a “calibration equation” that can be programmed into a patient monitor and used to determine analyte concentrations based on the measured reflectance spectra. Spectra collected from patients can be processed through calibration equation(s) stored in the patient monitor, and the analyte concentration in those patients can be reported based on the collected spectra and the calibration equations. Because the optical reflectance technique is noninvasive, the medical measurement can be updated as often as spectra are collected, usually on the order of a few seconds. The feasibility of using this method has been demonstrated on the bench, in animals, and in human subjects for the assessment of blood hematocrit, glucose, cholesterol, electrolytes, lactate, myoglobin saturation, muscle pH, and oxygen tension (“PO2”).
When calibration equations are developed using chemometrics, at least two sets of data are collected. A set of NIRS spectra is recorded approximately simultaneously with an independent, reliable measurement of the analyte over the entire physiologic and pathophysiologic range. For example, if one wanted to develop a calibration equation to determine blood hematocrit from measured reflectance spectra, several spectra from subjects would be compared with blood samples taken from those subjects and analyzed for hematocrit in a clinical laboratory. A chemometric technique, such as, for example, partial least squares (“PLS”) regression can be used to identify and correlate portions of the spectrum to the measured hematocrit. The regression coefficients are used to generate the calibration equation.
Then, when subsequent reflectance spectra are collected from other patients, the regression coefficients can be combined with the spectra of the other patients to produce the NIRS-determined hematocrit value for the other patients. An advantage of using a chemometric technique such as PLS to derive a calibration equation, rather than simple linear regression, is that PLS is adept at establishing correlation between spectra and analytes when the analyte spectra are complicated by other absorbing species and scattering elements (like cells and muscle fibers).
For the calibration equation to perform accurately on patients over a wide range of instrumental and environmental conditions and different patient characteristics, the data in the calibration data set should cover as wide a range of values as possible and should encompass the entire clinically significant range. It is also important that the data be collected under the type of variable patient conditions that might affect the NIRS spectra. Conditions that affect spectra include variation in temperature, water content, and the presence of interfering chemical agents used to treat the patient. This helps ensure that the calibration equations are accurate when used on future subjects, because the effect of the interfering agents is modeled as part of the calibration equation.
Widespread application of NIRS for medical measurement has been hampered by both inter- and intra-subject variation in tissue overlying the target tissue, such as muscle, or the target organ. Additionally, NIRS measurement techniques have been limited by their inaccurate performance due to short-term changes in skin blood flow or due to long-term variation in skin source and texture during wound healing.